Overview

Dataset statistics

Number of variables22
Number of observations37900
Missing cells14416
Missing cells (%)1.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.4 MiB
Average record size in memory176.0 B

Variable types

Text8
Numeric8
Categorical4
DateTime2

Alerts

payCardBank is highly overall correlated with payCardIDHigh correlation
payCardID is highly overall correlated with payCardBankHigh correlation
stopEndSeq is highly overall correlated with stopStartSeqHigh correlation
stopStartSeq is highly overall correlated with stopEndSeqHigh correlation
tapInStopsLat is highly overall correlated with tapOutStopsLatHigh correlation
tapInStopsLon is highly overall correlated with tapOutStopsLonHigh correlation
tapOutStopsLat is highly overall correlated with tapInStopsLatHigh correlation
tapOutStopsLon is highly overall correlated with tapInStopsLonHigh correlation
corridorID has 1257 (3.3%) missing valuesMissing
corridorName has 1930 (5.1%) missing valuesMissing
tapInStops has 1213 (3.2%) missing valuesMissing
tapOutStops has 2289 (6.0%) missing valuesMissing
tapOutStopsName has 1344 (3.5%) missing valuesMissing
tapOutStopsLat has 1344 (3.5%) missing valuesMissing
tapOutStopsLon has 1344 (3.5%) missing valuesMissing
stopEndSeq has 1344 (3.5%) missing valuesMissing
tapOutTime has 1344 (3.5%) missing valuesMissing
payAmount has 1007 (2.7%) missing valuesMissing
transID has unique valuesUnique
stopStartSeq has 2920 (7.7%) zerosZeros

Reproduction

Analysis started2023-12-26 16:52:50.675452
Analysis finished2023-12-26 16:53:22.796301
Duration32.12 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

transID
Text

UNIQUE 

Distinct37900
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size296.2 KiB
2023-12-26T16:53:23.120632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters530600
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37900 ?
Unique (%)100.0%

Sample

1st rowEIIW227B8L34VB
2nd rowLGXO740D2N47GZ
3rd rowDJWR385V2U57TO
4th rowJTUZ800U7C86EH
5th rowVMLO535V7F95NJ
ValueCountFrequency (%)
eiiw227b8l34vb 1
 
< 0.1%
zyrl615g4c05rd 1
 
< 0.1%
cupo258c0d42py 1
 
< 0.1%
oihs248v7s72eb 1
 
< 0.1%
djwr385v2u57to 1
 
< 0.1%
jtuz800u7c86eh 1
 
< 0.1%
vmlo535v7f95nj 1
 
< 0.1%
ddes630k2f80kc 1
 
< 0.1%
hemw326b9n91tv 1
 
< 0.1%
xtke052e5e87ln 1
 
< 0.1%
Other values (37890) 37890
> 99.9%
2023-12-26T16:53:23.817824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 22949
 
4.3%
6 22881
 
4.3%
8 22860
 
4.3%
9 22832
 
4.3%
4 22718
 
4.3%
3 22714
 
4.3%
1 22673
 
4.3%
7 22652
 
4.3%
2 22602
 
4.3%
0 22519
 
4.2%
Other values (26) 303200
57.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 303200
57.1%
Decimal Number 227400
42.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 11788
 
3.9%
C 11741
 
3.9%
D 11736
 
3.9%
R 11735
 
3.9%
S 11720
 
3.9%
U 11714
 
3.9%
K 11709
 
3.9%
P 11706
 
3.9%
J 11702
 
3.9%
Y 11701
 
3.9%
Other values (16) 185948
61.3%
Decimal Number
ValueCountFrequency (%)
5 22949
10.1%
6 22881
10.1%
8 22860
10.1%
9 22832
10.0%
4 22718
10.0%
3 22714
10.0%
1 22673
10.0%
7 22652
10.0%
2 22602
9.9%
0 22519
9.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 303200
57.1%
Common 227400
42.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 11788
 
3.9%
C 11741
 
3.9%
D 11736
 
3.9%
R 11735
 
3.9%
S 11720
 
3.9%
U 11714
 
3.9%
K 11709
 
3.9%
P 11706
 
3.9%
J 11702
 
3.9%
Y 11701
 
3.9%
Other values (16) 185948
61.3%
Common
ValueCountFrequency (%)
5 22949
10.1%
6 22881
10.1%
8 22860
10.1%
9 22832
10.0%
4 22718
10.0%
3 22714
10.0%
1 22673
10.0%
7 22652
10.0%
2 22602
9.9%
0 22519
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 530600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 22949
 
4.3%
6 22881
 
4.3%
8 22860
 
4.3%
9 22832
 
4.3%
4 22718
 
4.3%
3 22714
 
4.3%
1 22673
 
4.3%
7 22652
 
4.3%
2 22602
 
4.3%
0 22519
 
4.2%
Other values (26) 303200
57.1%

payCardID
Real number (ℝ)

HIGH CORRELATION 

Distinct2000
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2500603 × 1017
Minimum6.0403675 × 1010
Maximum4.9976939 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.2 KiB
2023-12-26T16:53:24.142828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6.0403675 × 1010
5-th percentile5.8905739 × 1011
Q11.8004424 × 1014
median3.5079466 × 1015
Q34.6990232 × 1015
95-th percentile4.4893373 × 1018
Maximum4.9976939 × 1018
Range4.9976939 × 1018
Interquartile range (IQR)4.518979 × 1015

Descriptive statistics

Standard deviation1.3216987 × 1018
Coefficient of variation (CV)3.1098351
Kurtosis6.0289022
Mean4.2500603 × 1017
Median Absolute Deviation (MAD)3.1293927 × 1015
Skewness2.8237921
Sum3.7209551 × 1018
Variance1.7468874 × 1036
MonotonicityNot monotonic
2023-12-26T16:53:24.466857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.800626598 × 101440
 
0.1%
2.273899077 × 101540
 
0.1%
3.533955956 × 101540
 
0.1%
5.250092672 × 101540
 
0.1%
4.739532514 × 101540
 
0.1%
3.480568175 × 101440
 
0.1%
3.721868523 × 101440
 
0.1%
3.014600091 × 101340
 
0.1%
4.489337334 × 101840
 
0.1%
2.259163638 × 101540
 
0.1%
Other values (1990) 37500
98.9%
ValueCountFrequency (%)
6.040367544 × 101040
0.1%
6.04044987 × 101040
0.1%
6.041702093 × 10104
 
< 0.1%
6.042120834 × 10104
 
< 0.1%
6.04275394 × 10101
 
< 0.1%
6.043882318 × 10104
 
< 0.1%
6.045913992 × 10101
 
< 0.1%
6.047717301 × 10104
 
< 0.1%
5.018263064 × 10111
 
< 0.1%
5.018281295 × 101140
0.1%
ValueCountFrequency (%)
4.997693931 × 101840
0.1%
4.995586293 × 10184
 
< 0.1%
4.986663118 × 101840
0.1%
4.980564692 × 101814
 
< 0.1%
4.980316891 × 10184
 
< 0.1%
4.980154954 × 101840
0.1%
4.965463576 × 101840
0.1%
4.963710913 × 101840
0.1%
4.958145745 × 101840
0.1%
4.957958811 × 101840
0.1%

payCardBank
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size296.2 KiB
dki
18743 
emoney
6866 
brizzi
3531 
flazz
3234 
online
2869 

Length

Max length6
Median length3
Mean length4.2207388
Min length3

Characters and Unicode

Total characters159966
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowemoney
2nd rowdki
3rd rowdki
4th rowflazz
5th rowflazz

Common Values

ValueCountFrequency (%)
dki 18743
49.5%
emoney 6866
 
18.1%
brizzi 3531
 
9.3%
flazz 3234
 
8.5%
online 2869
 
7.6%
bni 2657
 
7.0%

Length

2023-12-26T16:53:24.729149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-26T16:53:25.028192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
dki 18743
49.5%
emoney 6866
 
18.1%
brizzi 3531
 
9.3%
flazz 3234
 
8.5%
online 2869
 
7.6%
bni 2657
 
7.0%

Most occurring characters

ValueCountFrequency (%)
i 31331
19.6%
d 18743
11.7%
k 18743
11.7%
e 16601
10.4%
n 15261
9.5%
z 13530
8.5%
o 9735
 
6.1%
m 6866
 
4.3%
y 6866
 
4.3%
b 6188
 
3.9%
Other values (4) 16102
10.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 159966
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 31331
19.6%
d 18743
11.7%
k 18743
11.7%
e 16601
10.4%
n 15261
9.5%
z 13530
8.5%
o 9735
 
6.1%
m 6866
 
4.3%
y 6866
 
4.3%
b 6188
 
3.9%
Other values (4) 16102
10.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 159966
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 31331
19.6%
d 18743
11.7%
k 18743
11.7%
e 16601
10.4%
n 15261
9.5%
z 13530
8.5%
o 9735
 
6.1%
m 6866
 
4.3%
y 6866
 
4.3%
b 6188
 
3.9%
Other values (4) 16102
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 159966
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 31331
19.6%
d 18743
11.7%
k 18743
11.7%
e 16601
10.4%
n 15261
9.5%
z 13530
8.5%
o 9735
 
6.1%
m 6866
 
4.3%
y 6866
 
4.3%
b 6188
 
3.9%
Other values (4) 16102
10.1%
Distinct1993
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size296.2 KiB
2023-12-26T16:53:25.501645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length34
Median length29
Mean length17.271768
Min length9

Characters and Unicode

Total characters654600
Distinct characters54
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique299 ?
Unique (%)0.8%

Sample

1st rowBajragin Usada
2nd rowGandi Widodo
3rd rowEmong Wastuti
4th rowSurya Wacana
5th rowEmbuh Mardhiyah
ValueCountFrequency (%)
dr 1877
 
1.9%
drg 1115
 
1.2%
tgk 1037
 
1.1%
drs 962
 
1.0%
m.m 888
 
0.9%
r 795
 
0.8%
s.sos 656
 
0.7%
s.kom 639
 
0.7%
ir 613
 
0.6%
siregar 586
 
0.6%
Other values (828) 87534
90.5%
2023-12-26T16:53:26.287613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 112901
17.2%
58802
 
9.0%
i 54931
 
8.4%
r 37377
 
5.7%
n 37275
 
5.7%
t 26056
 
4.0%
. 24535
 
3.7%
u 23566
 
3.6%
s 20840
 
3.2%
d 18004
 
2.8%
Other values (44) 240313
36.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 453448
69.3%
Uppercase Letter 107652
 
16.4%
Space Separator 58802
 
9.0%
Other Punctuation 34698
 
5.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 16167
15.0%
M 10465
 
9.7%
P 9957
 
9.2%
H 7349
 
6.8%
K 5750
 
5.3%
R 5301
 
4.9%
A 5131
 
4.8%
D 4897
 
4.5%
N 4897
 
4.5%
I 4321
 
4.0%
Other values (16) 33417
31.0%
Lowercase Letter
ValueCountFrequency (%)
a 112901
24.9%
i 54931
12.1%
r 37377
 
8.2%
n 37275
 
8.2%
t 26056
 
5.7%
u 23566
 
5.2%
s 20840
 
4.6%
d 18004
 
4.0%
o 16782
 
3.7%
m 16609
 
3.7%
Other values (15) 89107
19.7%
Other Punctuation
ValueCountFrequency (%)
. 24535
70.7%
, 10163
29.3%
Space Separator
ValueCountFrequency (%)
58802
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 561100
85.7%
Common 93500
 
14.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 112901
20.1%
i 54931
 
9.8%
r 37377
 
6.7%
n 37275
 
6.6%
t 26056
 
4.6%
u 23566
 
4.2%
s 20840
 
3.7%
d 18004
 
3.2%
o 16782
 
3.0%
m 16609
 
3.0%
Other values (41) 196759
35.1%
Common
ValueCountFrequency (%)
58802
62.9%
. 24535
26.2%
, 10163
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 654600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 112901
17.2%
58802
 
9.0%
i 54931
 
8.4%
r 37377
 
5.7%
n 37275
 
5.7%
t 26056
 
4.0%
. 24535
 
3.7%
u 23566
 
3.6%
s 20840
 
3.2%
d 18004
 
2.8%
Other values (44) 240313
36.7%

payCardSex
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size296.2 KiB
F
20157 
M
17743 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37900
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowF
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
F 20157
53.2%
M 17743
46.8%

Length

2023-12-26T16:53:26.581683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-26T16:53:26.830526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
f 20157
53.2%
m 17743
46.8%

Most occurring characters

ValueCountFrequency (%)
F 20157
53.2%
M 17743
46.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 37900
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 20157
53.2%
M 17743
46.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 37900
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 20157
53.2%
M 17743
46.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 20157
53.2%
M 17743
46.8%

payCardBirthDate
Real number (ℝ)

Distinct67
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1990.0893
Minimum1946
Maximum2012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.2 KiB
2023-12-26T16:53:27.073737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1946
5-th percentile1967
Q11982
median1990
Q32001
95-th percentile2010
Maximum2012
Range66
Interquartile range (IQR)19

Descriptive statistics

Standard deviation13.051482
Coefficient of variation (CV)0.0065582394
Kurtosis-0.032965724
Mean1990.0893
Median Absolute Deviation (MAD)9
Skewness-0.41338542
Sum75424385
Variance170.34119
MonotonicityNot monotonic
2023-12-26T16:53:27.354901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1992 1394
 
3.7%
1981 1381
 
3.6%
1994 1355
 
3.6%
1987 1291
 
3.4%
1985 1251
 
3.3%
1984 1218
 
3.2%
1983 1186
 
3.1%
1989 1170
 
3.1%
1996 1140
 
3.0%
1991 1130
 
3.0%
Other values (57) 25384
67.0%
ValueCountFrequency (%)
1946 120
0.3%
1947 18
 
< 0.1%
1948 85
0.2%
1949 8
 
< 0.1%
1950 1
 
< 0.1%
1951 49
0.1%
1952 19
 
0.1%
1953 40
 
0.1%
1954 4
 
< 0.1%
1955 5
 
< 0.1%
ValueCountFrequency (%)
2012 800
2.1%
2011 730
1.9%
2010 747
2.0%
2009 754
2.0%
2008 880
2.3%
2007 808
2.1%
2006 693
1.8%
2005 711
1.9%
2004 906
2.4%
2003 935
2.5%

corridorID
Text

MISSING 

Distinct221
Distinct (%)0.6%
Missing1257
Missing (%)3.3%
Memory size296.2 KiB
2023-12-26T16:53:28.139763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.7205196
Min length1

Characters and Unicode

Total characters136331
Distinct characters32
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row6C
3rd rowR1A
4th row11D
5th row12
ValueCountFrequency (%)
1t 400
 
1.1%
s21 388
 
1.1%
jis3 341
 
0.9%
8c 339
 
0.9%
jak.06 333
 
0.9%
11p 332
 
0.9%
2e 318
 
0.9%
9d 310
 
0.8%
m7b 309
 
0.8%
jak.72 305
 
0.8%
Other values (211) 33268
90.8%
2023-12-26T16:53:29.073550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 18143
13.3%
A 15451
11.3%
K 14129
10.4%
J 13785
10.1%
. 13444
 
9.9%
2 7406
 
5.4%
3 6088
 
4.5%
4 5141
 
3.8%
6 4559
 
3.3%
0 4224
 
3.1%
Other values (22) 33961
24.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 63602
46.7%
Decimal Number 59285
43.5%
Other Punctuation 13444
 
9.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 15451
24.3%
K 14129
22.2%
J 13785
21.7%
B 3070
 
4.8%
M 2730
 
4.3%
D 2294
 
3.6%
C 2222
 
3.5%
E 1305
 
2.1%
S 1235
 
1.9%
H 1105
 
1.7%
Other values (11) 6276
9.9%
Decimal Number
ValueCountFrequency (%)
1 18143
30.6%
2 7406
12.5%
3 6088
 
10.3%
4 5141
 
8.7%
6 4559
 
7.7%
0 4224
 
7.1%
8 3822
 
6.4%
7 3705
 
6.2%
5 3440
 
5.8%
9 2757
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 13444
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 72729
53.3%
Latin 63602
46.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 15451
24.3%
K 14129
22.2%
J 13785
21.7%
B 3070
 
4.8%
M 2730
 
4.3%
D 2294
 
3.6%
C 2222
 
3.5%
E 1305
 
2.1%
S 1235
 
1.9%
H 1105
 
1.7%
Other values (11) 6276
9.9%
Common
ValueCountFrequency (%)
1 18143
24.9%
. 13444
18.5%
2 7406
10.2%
3 6088
 
8.4%
4 5141
 
7.1%
6 4559
 
6.3%
0 4224
 
5.8%
8 3822
 
5.3%
7 3705
 
5.1%
5 3440
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 136331
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 18143
13.3%
A 15451
11.3%
K 14129
10.4%
J 13785
10.1%
. 13444
 
9.9%
2 7406
 
5.4%
3 6088
 
4.5%
4 5141
 
3.8%
6 4559
 
3.3%
0 4224
 
3.1%
Other values (22) 33961
24.9%

corridorName
Text

MISSING 

Distinct216
Distinct (%)0.6%
Missing1930
Missing (%)5.1%
Memory size296.2 KiB
2023-12-26T16:53:29.536811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length51
Median length40
Mean length26.138782
Min length11

Characters and Unicode

Total characters940212
Distinct characters51
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMatraman Baru - Ancol
2nd rowStasiun Tebet - Karet via Patra Kuningan
3rd rowPantai Maju - Kota
4th rowPulo Gebang - Pulo Gadung 2 via PIK
5th rowTanjung Priok - Pluit
ValueCountFrequency (%)
36329
 
21.1%
via 4214
 
2.4%
pulo 4108
 
2.4%
kampung 4070
 
2.4%
rusun 3838
 
2.2%
m 3135
 
1.8%
blok 3135
 
1.8%
tanah 2981
 
1.7%
gadung 2596
 
1.5%
abang 2527
 
1.5%
Other values (241) 105345
61.1%
2023-12-26T16:53:30.341945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
136308
14.5%
a 128608
13.7%
n 85832
 
9.1%
u 54979
 
5.8%
i 42976
 
4.6%
e 38167
 
4.1%
- 36329
 
3.9%
r 34290
 
3.6%
g 33034
 
3.5%
o 29306
 
3.1%
Other values (41) 320383
34.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 623275
66.3%
Uppercase Letter 139427
 
14.8%
Space Separator 136308
 
14.5%
Dash Punctuation 36329
 
3.9%
Decimal Number 2252
 
0.2%
Other Punctuation 1503
 
0.2%
Open Punctuation 559
 
0.1%
Close Punctuation 559
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 128608
20.6%
n 85832
13.8%
u 54979
8.8%
i 42976
 
6.9%
e 38167
 
6.1%
r 34290
 
5.5%
g 33034
 
5.3%
o 29306
 
4.7%
l 27736
 
4.5%
t 25491
 
4.1%
Other values (13) 122856
19.7%
Uppercase Letter
ValueCountFrequency (%)
P 20451
14.7%
K 16476
11.8%
B 15667
11.2%
T 13206
9.5%
R 13067
9.4%
M 10655
7.6%
S 9186
6.6%
G 8372
6.0%
C 7834
 
5.6%
A 5232
 
3.8%
Other values (11) 19281
13.8%
Decimal Number
ValueCountFrequency (%)
2 2098
93.2%
1 154
 
6.8%
Space Separator
ValueCountFrequency (%)
136308
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36329
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1503
100.0%
Open Punctuation
ValueCountFrequency (%)
( 559
100.0%
Close Punctuation
ValueCountFrequency (%)
) 559
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 762702
81.1%
Common 177510
 
18.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 128608
16.9%
n 85832
 
11.3%
u 54979
 
7.2%
i 42976
 
5.6%
e 38167
 
5.0%
r 34290
 
4.5%
g 33034
 
4.3%
o 29306
 
3.8%
l 27736
 
3.6%
t 25491
 
3.3%
Other values (34) 262283
34.4%
Common
ValueCountFrequency (%)
136308
76.8%
- 36329
 
20.5%
2 2098
 
1.2%
. 1503
 
0.8%
( 559
 
0.3%
) 559
 
0.3%
1 154
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 940212
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
136308
14.5%
a 128608
13.7%
n 85832
 
9.1%
u 54979
 
5.8%
i 42976
 
4.6%
e 38167
 
4.1%
- 36329
 
3.9%
r 34290
 
3.6%
g 33034
 
3.5%
o 29306
 
3.1%
Other values (41) 320383
34.1%

direction
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size296.2 KiB
1.0
18974 
0.0
18926 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters113700
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 18974
50.1%
0.0 18926
49.9%

Length

2023-12-26T16:53:30.646623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-26T16:53:30.898638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 18974
50.1%
0.0 18926
49.9%

Most occurring characters

ValueCountFrequency (%)
0 56826
50.0%
. 37900
33.3%
1 18974
 
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 75800
66.7%
Other Punctuation 37900
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 56826
75.0%
1 18974
 
25.0%
Other Punctuation
ValueCountFrequency (%)
. 37900
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 113700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 56826
50.0%
. 37900
33.3%
1 18974
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 113700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 56826
50.0%
. 37900
33.3%
1 18974
 
16.7%

tapInStops
Text

MISSING 

Distinct2570
Distinct (%)7.0%
Missing1213
Missing (%)3.2%
Memory size296.2 KiB
2023-12-26T16:53:31.294899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.7263881
Min length6

Characters and Unicode

Total characters246771
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique910 ?
Unique (%)2.5%

Sample

1st rowP00142
2nd rowB01963P
3rd rowB00499P
4th rowB05587P
5th rowP00239
ValueCountFrequency (%)
p00170 236
 
0.6%
p00064 200
 
0.5%
p00016 170
 
0.5%
p00297 151
 
0.4%
p00164 145
 
0.4%
b00248p 130
 
0.4%
p00169 127
 
0.3%
p00221 123
 
0.3%
b03277p 122
 
0.3%
p00254 122
 
0.3%
Other values (2560) 35161
95.8%
2023-12-26T16:53:32.041135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 66069
26.8%
P 36687
14.9%
B 26649
10.8%
2 17683
 
7.2%
1 17304
 
7.0%
5 13878
 
5.6%
3 13846
 
5.6%
6 12529
 
5.1%
4 12459
 
5.0%
8 10518
 
4.3%
Other values (2) 19149
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 183435
74.3%
Uppercase Letter 63336
 
25.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 66069
36.0%
2 17683
 
9.6%
1 17304
 
9.4%
5 13878
 
7.6%
3 13846
 
7.5%
6 12529
 
6.8%
4 12459
 
6.8%
8 10518
 
5.7%
7 10267
 
5.6%
9 8882
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
P 36687
57.9%
B 26649
42.1%

Most occurring scripts

ValueCountFrequency (%)
Common 183435
74.3%
Latin 63336
 
25.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 66069
36.0%
2 17683
 
9.6%
1 17304
 
9.4%
5 13878
 
7.6%
3 13846
 
7.5%
6 12529
 
6.8%
4 12459
 
6.8%
8 10518
 
5.7%
7 10267
 
5.6%
9 8882
 
4.8%
Latin
ValueCountFrequency (%)
P 36687
57.9%
B 26649
42.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 246771
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 66069
26.8%
P 36687
14.9%
B 26649
10.8%
2 17683
 
7.2%
1 17304
 
7.0%
5 13878
 
5.6%
3 13846
 
5.6%
6 12529
 
5.1%
4 12459
 
5.0%
8 10518
 
4.3%
Other values (2) 19149
 
7.8%
Distinct2602
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size296.2 KiB
2023-12-26T16:53:32.790395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length41
Median length33
Mean length16.584354
Min length3

Characters and Unicode

Total characters628547
Distinct characters66
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique929 ?
Unique (%)2.5%

Sample

1st rowPal Putih
2nd rowKemenkes 2
3rd rowGg. Kunir II
4th rowTaman Elok 1
5th rowSunter Boulevard Barat
ValueCountFrequency (%)
jln 4613
 
4.2%
2 3678
 
3.3%
1 3526
 
3.2%
sbr 3456
 
3.1%
barat 1445
 
1.3%
timur 1395
 
1.3%
arah 1257
 
1.1%
simpang 1254
 
1.1%
raya 1142
 
1.0%
gg 1074
 
1.0%
Other values (1904) 87231
79.2%
2023-12-26T16:53:34.138390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 87431
 
13.9%
72171
 
11.5%
n 45026
 
7.2%
r 33345
 
5.3%
i 30172
 
4.8%
e 27831
 
4.4%
u 23586
 
3.8%
l 20324
 
3.2%
t 19482
 
3.1%
g 17587
 
2.8%
Other values (56) 251592
40.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 414716
66.0%
Uppercase Letter 117986
 
18.8%
Space Separator 72171
 
11.5%
Other Punctuation 12204
 
1.9%
Decimal Number 11399
 
1.8%
Dash Punctuation 71
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 87431
21.1%
n 45026
10.9%
r 33345
 
8.0%
i 30172
 
7.3%
e 27831
 
6.7%
u 23586
 
5.7%
l 20324
 
4.9%
t 19482
 
4.7%
g 17587
 
4.2%
m 16486
 
4.0%
Other values (16) 93446
22.5%
Uppercase Letter
ValueCountFrequency (%)
S 15164
12.9%
P 13315
11.3%
B 9444
 
8.0%
K 9316
 
7.9%
J 8940
 
7.6%
M 8498
 
7.2%
T 7768
 
6.6%
R 6223
 
5.3%
A 6074
 
5.1%
I 6032
 
5.1%
Other values (16) 27212
23.1%
Decimal Number
ValueCountFrequency (%)
1 4141
36.3%
2 4028
35.3%
0 783
 
6.9%
3 693
 
6.1%
5 566
 
5.0%
7 334
 
2.9%
4 327
 
2.9%
8 211
 
1.9%
6 200
 
1.8%
9 116
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 12198
> 99.9%
/ 6
 
< 0.1%
Space Separator
ValueCountFrequency (%)
72171
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 71
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 532702
84.8%
Common 95845
 
15.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 87431
16.4%
n 45026
 
8.5%
r 33345
 
6.3%
i 30172
 
5.7%
e 27831
 
5.2%
u 23586
 
4.4%
l 20324
 
3.8%
t 19482
 
3.7%
g 17587
 
3.3%
m 16486
 
3.1%
Other values (42) 211432
39.7%
Common
ValueCountFrequency (%)
72171
75.3%
. 12198
 
12.7%
1 4141
 
4.3%
2 4028
 
4.2%
0 783
 
0.8%
3 693
 
0.7%
5 566
 
0.6%
7 334
 
0.3%
4 327
 
0.3%
8 211
 
0.2%
Other values (4) 393
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 628547
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 87431
 
13.9%
72171
 
11.5%
n 45026
 
7.2%
r 33345
 
5.3%
i 30172
 
4.8%
e 27831
 
4.4%
u 23586
 
3.8%
l 20324
 
3.2%
t 19482
 
3.1%
g 17587
 
2.8%
Other values (56) 251592
40.0%

tapInStopsLat
Real number (ℝ)

HIGH CORRELATION 

Distinct2587
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.2148379
Minimum-6.394973
Maximum-6.089429
Zeros0
Zeros (%)0.0%
Negative37900
Negative (%)100.0%
Memory size296.2 KiB
2023-12-26T16:53:34.656392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-6.394973
5-th percentile-6.3126762
Q1-6.245863
median-6.214587
Q3-6.175528
95-th percentile-6.120279
Maximum-6.089429
Range0.305544
Interquartile range (IQR)0.070335

Descriptive statistics

Standard deviation0.057911227
Coefficient of variation (CV)-0.00931822
Kurtosis-0.24023203
Mean-6.2148379
Median Absolute Deviation (MAD)0.035872
Skewness-0.25374441
Sum-235542.36
Variance0.0033537102
MonotonicityNot monotonic
2023-12-26T16:53:35.016693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-6.126306 243
 
0.6%
-6.290154 208
 
0.5%
-6.257751 173
 
0.5%
-6.245863 155
 
0.4%
-6.278404 152
 
0.4%
-6.214132 137
 
0.4%
-6.368735 133
 
0.4%
-6.135667 131
 
0.3%
-6.240213 126
 
0.3%
-6.238064 126
 
0.3%
Other values (2577) 36316
95.8%
ValueCountFrequency (%)
-6.394973 40
0.1%
-6.387532 8
 
< 0.1%
-6.387291 22
0.1%
-6.384603 2
 
< 0.1%
-6.382532 1
 
< 0.1%
-6.381864 1
 
< 0.1%
-6.380018 1
 
< 0.1%
-6.379677 1
 
< 0.1%
-6.379631 7
 
< 0.1%
-6.379606 1
 
< 0.1%
ValueCountFrequency (%)
-6.089429 20
 
0.1%
-6.091746 20
 
0.1%
-6.091992 1
 
< 0.1%
-6.093258 5
 
< 0.1%
-6.093637 63
0.2%
-6.096407 1
 
< 0.1%
-6.097099 20
 
0.1%
-6.097476 2
 
< 0.1%
-6.09791 8
 
< 0.1%
-6.098023 1
 
< 0.1%

tapInStopsLon
Real number (ℝ)

HIGH CORRELATION 

Distinct2458
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.84155
Minimum106.61473
Maximum107.02395
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.2 KiB
2023-12-26T16:53:35.455261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum106.61473
5-th percentile106.74598
Q1106.80347
median106.83483
Q3106.88227
95-th percentile106.93961
Maximum107.02395
Range0.40922
Interquartile range (IQR)0.0788

Descriptive statistics

Standard deviation0.060369455
Coefficient of variation (CV)0.00056503722
Kurtosis0.58176478
Mean106.84155
Median Absolute Deviation (MAD)0.039245
Skewness-0.11928473
Sum4049294.9
Variance0.0036444711
MonotonicityNot monotonic
2023-12-26T16:53:35.832746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
106.79203 243
 
0.6%
106.88116 208
 
0.5%
106.87 173
 
0.5%
106.871143 155
 
0.4%
106.82972 152
 
0.4%
106.89366 133
 
0.4%
106.76299 131
 
0.3%
106.93961 130
 
0.3%
106.83102 127
 
0.3%
106.81624 127
 
0.3%
Other values (2448) 36321
95.8%
ValueCountFrequency (%)
106.61473 41
0.1%
106.61667 1
 
< 0.1%
106.61807 21
0.1%
106.61882 20
0.1%
106.61888 1
 
< 0.1%
106.61931 1
 
< 0.1%
106.61943 1
 
< 0.1%
106.62023 21
0.1%
106.62026 21
0.1%
106.62391 21
0.1%
ValueCountFrequency (%)
107.02395 21
0.1%
107.02384 20
0.1%
107.02303 20
0.1%
107.02206 20
0.1%
107.02153 20
0.1%
107.02149 1
 
< 0.1%
107.02075 1
 
< 0.1%
107.02069 27
0.1%
107.0206 20
0.1%
107.01987 8
 
< 0.1%

stopStartSeq
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct67
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.57248
Minimum0
Maximum68
Zeros2920
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size296.2 KiB
2023-12-26T16:53:36.265663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median10
Q319
95-th percentile39
Maximum68
Range68
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.237623
Coefficient of variation (CV)0.9016497
Kurtosis1.2655431
Mean13.57248
Median Absolute Deviation (MAD)7
Skewness1.2444309
Sum514397
Variance149.75941
MonotonicityNot monotonic
2023-12-26T16:53:36.711126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2920
 
7.7%
2 2026
 
5.3%
4 1792
 
4.7%
5 1789
 
4.7%
1 1683
 
4.4%
3 1612
 
4.3%
11 1590
 
4.2%
6 1527
 
4.0%
8 1521
 
4.0%
7 1519
 
4.0%
Other values (57) 19921
52.6%
ValueCountFrequency (%)
0 2920
7.7%
1 1683
4.4%
2 2026
5.3%
3 1612
4.3%
4 1792
4.7%
5 1789
4.7%
6 1527
4.0%
7 1519
4.0%
8 1521
4.0%
9 1370
3.6%
ValueCountFrequency (%)
68 8
 
< 0.1%
66 1
 
< 0.1%
65 21
0.1%
64 2
 
< 0.1%
63 8
 
< 0.1%
62 41
0.1%
61 28
0.1%
59 2
 
< 0.1%
58 25
0.1%
57 41
0.1%
Distinct37079
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size296.2 KiB
Minimum2023-04-01 06:22:27
Maximum2023-04-30 21:55:41
2023-12-26T16:53:37.130177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:37.620948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

tapOutStops
Text

MISSING 

Distinct2230
Distinct (%)6.3%
Missing2289
Missing (%)6.0%
Memory size296.2 KiB
2023-12-26T16:53:38.554146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.7137963
Min length6

Characters and Unicode

Total characters239085
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique762 ?
Unique (%)2.1%

Sample

1st rowP00253
2nd rowB03307P
3rd rowB04962P
4th rowB03090P
5th rowP00098
ValueCountFrequency (%)
p00016 306
 
0.9%
p00170 255
 
0.7%
b05725p 189
 
0.5%
p00137 161
 
0.5%
b05708p 158
 
0.4%
p00112 158
 
0.4%
p00199 146
 
0.4%
p00179 140
 
0.4%
b03396p 131
 
0.4%
p00254 127
 
0.4%
Other values (2220) 33840
95.0%
2023-12-26T16:53:39.322359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 63397
26.5%
P 35611
14.9%
B 25419
10.6%
2 17413
 
7.3%
1 17293
 
7.2%
5 13730
 
5.7%
3 13119
 
5.5%
6 11737
 
4.9%
4 11539
 
4.8%
7 10385
 
4.3%
Other values (2) 19442
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 178055
74.5%
Uppercase Letter 61030
 
25.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 63397
35.6%
2 17413
 
9.8%
1 17293
 
9.7%
5 13730
 
7.7%
3 13119
 
7.4%
6 11737
 
6.6%
4 11539
 
6.5%
7 10385
 
5.8%
8 10132
 
5.7%
9 9310
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
P 35611
58.3%
B 25419
41.7%

Most occurring scripts

ValueCountFrequency (%)
Common 178055
74.5%
Latin 61030
 
25.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 63397
35.6%
2 17413
 
9.8%
1 17293
 
9.7%
5 13730
 
7.7%
3 13119
 
7.4%
6 11737
 
6.6%
4 11539
 
6.5%
7 10385
 
5.8%
8 10132
 
5.7%
9 9310
 
5.2%
Latin
ValueCountFrequency (%)
P 35611
58.3%
B 25419
41.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 239085
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 63397
26.5%
P 35611
14.9%
B 25419
10.6%
2 17413
 
7.3%
1 17293
 
7.2%
5 13730
 
5.7%
3 13119
 
5.5%
6 11737
 
4.9%
4 11539
 
4.8%
7 10385
 
4.3%
Other values (2) 19442
 
8.1%

tapOutStopsName
Text

MISSING 

Distinct2248
Distinct (%)6.1%
Missing1344
Missing (%)3.5%
Memory size296.2 KiB
2023-12-26T16:53:39.867915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length42
Median length33
Mean length16.333981
Min length3

Characters and Unicode

Total characters597105
Distinct characters66
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique775 ?
Unique (%)2.1%

Sample

1st rowTegalan
2nd rowSampoerna Strategic
3rd rowSimpang Kunir Kemukus
4th rowRaya Penggilingan
5th rowKali Besar Barat
ValueCountFrequency (%)
2 3890
 
3.7%
jln 3777
 
3.6%
1 3021
 
2.9%
sbr 2784
 
2.7%
simpang 1577
 
1.5%
raya 1214
 
1.2%
timur 1164
 
1.1%
barat 1094
 
1.0%
arah 1088
 
1.0%
masjid 946
 
0.9%
Other values (1723) 84259
80.4%
2023-12-26T16:53:40.734247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 83372
 
14.0%
68258
 
11.4%
n 43100
 
7.2%
r 30886
 
5.2%
i 27619
 
4.6%
e 25535
 
4.3%
u 24042
 
4.0%
t 18885
 
3.2%
l 18730
 
3.1%
g 17653
 
3.0%
Other values (56) 239025
40.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 393778
65.9%
Uppercase Letter 112800
 
18.9%
Space Separator 68258
 
11.4%
Other Punctuation 11436
 
1.9%
Decimal Number 10816
 
1.8%
Dash Punctuation 17
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 83372
21.2%
n 43100
10.9%
r 30886
 
7.8%
i 27619
 
7.0%
e 25535
 
6.5%
u 24042
 
6.1%
t 18885
 
4.8%
l 18730
 
4.8%
g 17653
 
4.5%
m 17013
 
4.3%
Other values (16) 86943
22.1%
Uppercase Letter
ValueCountFrequency (%)
S 14484
12.8%
P 13448
11.9%
K 9432
 
8.4%
J 8286
 
7.3%
B 8164
 
7.2%
T 8080
 
7.2%
M 7852
 
7.0%
R 7074
 
6.3%
I 5749
 
5.1%
A 5717
 
5.1%
Other values (15) 24514
21.7%
Decimal Number
ValueCountFrequency (%)
2 4359
40.3%
1 3433
31.7%
3 844
 
7.8%
0 633
 
5.9%
5 358
 
3.3%
4 352
 
3.3%
7 295
 
2.7%
8 243
 
2.2%
6 161
 
1.5%
9 138
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 11434
> 99.9%
& 1
 
< 0.1%
/ 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
68258
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 506578
84.8%
Common 90527
 
15.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 83372
16.5%
n 43100
 
8.5%
r 30886
 
6.1%
i 27619
 
5.5%
e 25535
 
5.0%
u 24042
 
4.7%
t 18885
 
3.7%
l 18730
 
3.7%
g 17653
 
3.5%
m 17013
 
3.4%
Other values (41) 199743
39.4%
Common
ValueCountFrequency (%)
68258
75.4%
. 11434
 
12.6%
2 4359
 
4.8%
1 3433
 
3.8%
3 844
 
0.9%
0 633
 
0.7%
5 358
 
0.4%
4 352
 
0.4%
7 295
 
0.3%
8 243
 
0.3%
Other values (5) 318
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 597105
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 83372
 
14.0%
68258
 
11.4%
n 43100
 
7.2%
r 30886
 
5.2%
i 27619
 
4.6%
e 25535
 
4.3%
u 24042
 
4.0%
t 18885
 
3.2%
l 18730
 
3.1%
g 17653
 
3.0%
Other values (56) 239025
40.0%

tapOutStopsLat
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2237
Distinct (%)6.1%
Missing1344
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean-6.2146513
Minimum-6.394973
Maximum-6.091746
Zeros0
Zeros (%)0.0%
Negative36556
Negative (%)96.5%
Memory size296.2 KiB
2023-12-26T16:53:41.066790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-6.394973
5-th percentile-6.3125753
Q1-6.247225
median-6.214718
Q3-6.174736
95-th percentile-6.116512
Maximum-6.091746
Range0.303227
Interquartile range (IQR)0.072489

Descriptive statistics

Standard deviation0.059022026
Coefficient of variation (CV)-0.0094972385
Kurtosis-0.34313281
Mean-6.2146513
Median Absolute Deviation (MAD)0.03847
Skewness-0.22590932
Sum-227182.79
Variance0.0034835995
MonotonicityNot monotonic
2023-12-26T16:53:41.350060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-6.257751 316
 
0.8%
-6.126306 265
 
0.7%
-6.17356 196
 
0.5%
-6.176248 167
 
0.4%
-6.308964 164
 
0.4%
-6.2405 159
 
0.4%
-6.305769 151
 
0.4%
-6.291075 146
 
0.4%
-6.224688 131
 
0.3%
-6.115795 131
 
0.3%
Other values (2227) 34730
91.6%
(Missing) 1344
 
3.5%
ValueCountFrequency (%)
-6.394973 1
 
< 0.1%
-6.391068 45
0.1%
-6.387532 1
 
< 0.1%
-6.383095 1
 
< 0.1%
-6.382532 1
 
< 0.1%
-6.381864 1
 
< 0.1%
-6.380896 2
 
< 0.1%
-6.380018 19
0.1%
-6.379679 7
 
< 0.1%
-6.379677 1
 
< 0.1%
ValueCountFrequency (%)
-6.091746 25
 
0.1%
-6.092201 41
0.1%
-6.093258 21
 
0.1%
-6.093637 77
0.2%
-6.094772 2
 
< 0.1%
-6.097099 26
 
0.1%
-6.09791 20
 
0.1%
-6.098204 20
 
0.1%
-6.09826 25
 
0.1%
-6.099616 1
 
< 0.1%

tapOutStopsLon
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2139
Distinct (%)5.9%
Missing1344
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean106.84123
Minimum106.61473
Maximum107.02366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.2 KiB
2023-12-26T16:53:41.643775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum106.61473
5-th percentile106.74443
Q1106.80175
median106.83458
Q3106.88303
95-th percentile106.94252
Maximum107.02366
Range0.40893
Interquartile range (IQR)0.08128

Descriptive statistics

Standard deviation0.060999459
Coefficient of variation (CV)0.00057093555
Kurtosis0.45242034
Mean106.84123
Median Absolute Deviation (MAD)0.03968
Skewness-0.096326789
Sum3905688.1
Variance0.003720934
MonotonicityNot monotonic
2023-12-26T16:53:41.960581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
106.87 316
 
0.8%
106.79203 265
 
0.7%
106.80188 198
 
0.5%
106.8657 197
 
0.5%
106.84213 196
 
0.5%
106.82286 167
 
0.4%
106.88165 164
 
0.4%
106.79844 159
 
0.4%
106.83102 152
 
0.4%
106.81949 151
 
0.4%
Other values (2129) 34591
91.3%
(Missing) 1344
 
3.5%
ValueCountFrequency (%)
106.61473 20
0.1%
106.61589 20
0.1%
106.61649 22
0.1%
106.61667 1
 
< 0.1%
106.61807 41
0.1%
106.61844 1
 
< 0.1%
106.61918 1
 
< 0.1%
106.61931 8
 
< 0.1%
106.62187 20
0.1%
106.63364 2
 
< 0.1%
ValueCountFrequency (%)
107.02366 20
0.1%
107.02344 1
 
< 0.1%
107.02206 1
 
< 0.1%
107.02205 1
 
< 0.1%
107.02153 38
0.1%
107.02125 1
 
< 0.1%
107.02069 20
0.1%
107.0206 8
 
< 0.1%
107.01988 21
0.1%
107.01858 7
 
< 0.1%

stopEndSeq
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct74
Distinct (%)0.2%
Missing1344
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean21.219909
Minimum1
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.2 KiB
2023-12-26T16:53:42.266567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median18
Q329
95-th percentile48
Maximum77
Range76
Interquartile range (IQR)18

Descriptive statistics

Standard deviation13.800689
Coefficient of variation (CV)0.65036512
Kurtosis0.14306451
Mean21.219909
Median Absolute Deviation (MAD)8
Skewness0.81538457
Sum775715
Variance190.45901
MonotonicityNot monotonic
2023-12-26T16:53:42.561588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 1462
 
3.9%
15 1309
 
3.5%
13 1297
 
3.4%
14 1294
 
3.4%
6 1278
 
3.4%
16 1268
 
3.3%
19 1189
 
3.1%
10 1178
 
3.1%
18 1167
 
3.1%
11 1142
 
3.0%
Other values (64) 23972
63.3%
(Missing) 1344
 
3.5%
ValueCountFrequency (%)
1 732
1.9%
2 694
1.8%
3 568
1.5%
4 739
1.9%
5 933
2.5%
6 1278
3.4%
7 697
1.8%
8 807
2.1%
9 957
2.5%
10 1178
3.1%
ValueCountFrequency (%)
77 1
 
< 0.1%
73 19
 
0.1%
72 1
 
< 0.1%
71 1
 
< 0.1%
70 2
 
< 0.1%
69 9
 
< 0.1%
68 2
 
< 0.1%
67 21
0.1%
66 51
0.1%
65 22
0.1%

tapOutTime
Date

MISSING 

Distinct35908
Distinct (%)98.2%
Missing1344
Missing (%)3.5%
Memory size296.2 KiB
Minimum2023-04-01 07:27:31
Maximum2023-04-30 23:23:18
2023-12-26T16:53:42.859801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:43.183350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

payAmount
Categorical

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing1007
Missing (%)2.7%
Memory size296.2 KiB
3500.0
18503 
0.0
16648 
20000.0
 
1742

Length

Max length7
Median length6
Mean length4.6934649
Min length3

Characters and Unicode

Total characters173156
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3500.0
2nd row3500.0
3rd row3500.0
4th row3500.0
5th row3500.0

Common Values

ValueCountFrequency (%)
3500.0 18503
48.8%
0.0 16648
43.9%
20000.0 1742
 
4.6%
(Missing) 1007
 
2.7%

Length

2023-12-26T16:53:43.459019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-26T16:53:43.726962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3500.0 18503
50.2%
0.0 16648
45.1%
20000.0 1742
 
4.7%

Most occurring characters

ValueCountFrequency (%)
0 97515
56.3%
. 36893
 
21.3%
3 18503
 
10.7%
5 18503
 
10.7%
2 1742
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 136263
78.7%
Other Punctuation 36893
 
21.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97515
71.6%
3 18503
 
13.6%
5 18503
 
13.6%
2 1742
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 36893
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 173156
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97515
56.3%
. 36893
 
21.3%
3 18503
 
10.7%
5 18503
 
10.7%
2 1742
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 173156
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97515
56.3%
. 36893
 
21.3%
3 18503
 
10.7%
5 18503
 
10.7%
2 1742
 
1.0%

Interactions

2023-12-26T16:53:16.261879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:52:59.369678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:02.214511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:05.342364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:07.694051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:09.716759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:12.021407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:14.116976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:16.622622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:52:59.660613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:02.609065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:05.640728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:07.943913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:09.972852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:12.271294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:14.369531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:16.967641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:52:59.921316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:02.985009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:06.032400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:08.188526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:10.228733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:12.528938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:14.642644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:17.344813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:00.201576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:03.361007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:06.383514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:08.441277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:10.712747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:12.797813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:14.925745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:17.743115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:00.563503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:03.760041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:06.640531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:08.677074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:10.962167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:13.043132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:15.172761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:18.131823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:00.870350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:04.096462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:06.898976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:08.936144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:11.215047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:13.310333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:15.439284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:18.537464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:01.454301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:04.495637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:07.151659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:09.184537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:11.472661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:13.565084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:15.706586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:18.972459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:01.864616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:04.926196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:07.417959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:09.451407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:11.758054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:13.855397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-26T16:53:15.992335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-12-26T16:53:43.926049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
directionpayAmountpayCardBankpayCardBirthDatepayCardIDpayCardSexstopEndSeqstopStartSeqtapInStopsLattapInStopsLontapOutStopsLattapOutStopsLon
direction1.0000.0000.0000.001-0.0010.000-0.035-0.0450.005-0.006-0.0500.061
payAmount0.0001.0000.0740.075-0.0340.017-0.386-0.265-0.131-0.296-0.130-0.287
payCardBank0.0000.0741.000-0.011-0.6360.070-0.008-0.006-0.0170.024-0.0220.030
payCardBirthDate0.0010.075-0.0111.000-0.0300.269-0.023-0.0250.053-0.0530.034-0.044
payCardID-0.001-0.034-0.636-0.0301.0000.008-0.0010.0070.027-0.0390.024-0.047
payCardSex0.0000.0170.0700.2690.0081.000-0.017-0.0120.0120.0330.0170.020
stopEndSeq-0.035-0.386-0.008-0.023-0.001-0.0171.0000.792-0.0210.089-0.0280.081
stopStartSeq-0.045-0.265-0.006-0.0250.007-0.0120.7921.000-0.0080.053-0.0240.045
tapInStopsLat0.005-0.131-0.0170.0530.0270.012-0.021-0.0081.0000.0240.9110.033
tapInStopsLon-0.006-0.2960.024-0.053-0.0390.0330.0890.0530.0241.0000.0330.914
tapOutStopsLat-0.050-0.130-0.0220.0340.0240.017-0.028-0.0240.9110.0331.0000.014
tapOutStopsLon0.061-0.2870.030-0.044-0.0470.0200.0810.0450.0330.9140.0141.000

Missing values

2023-12-26T16:53:19.759302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-26T16:53:21.094666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-26T16:53:22.459111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

transIDpayCardIDpayCardBankpayCardNamepayCardSexpayCardBirthDatecorridorIDcorridorNamedirectiontapInStopstapInStopsNametapInStopsLattapInStopsLonstopStartSeqtapInTimetapOutStopstapOutStopsNametapOutStopsLattapOutStopsLonstopEndSeqtapOutTimepayAmount
0EIIW227B8L34VB180062659848800emoneyBajragin UsadaM20085Matraman Baru - Ancol1.0P00142Pal Putih-6.184631106.8440272023-04-03 05:21:44P00253Tegalan-6.203101106.8571512.02023-04-03 06:00:533500.0
1LGXO740D2N47GZ4885331907664776dkiGandi WidodoF19976CStasiun Tebet - Karet via Patra Kuningan0.0B01963PKemenkes 2-6.228700106.83302132023-04-03 05:42:44B03307PSampoerna Strategic-6.217152106.8189221.02023-04-03 06:40:013500.0
2DJWR385V2U57TO4996225095064169dkiEmong WastutiF1992R1APantai Maju - Kota0.0B00499PGg. Kunir II-6.133132106.81435382023-04-03 05:59:06B04962PSimpang Kunir Kemukus-6.133731106.8147539.02023-04-03 06:50:553500.0
3JTUZ800U7C86EH639099174703flazzSurya WacanaF197811DPulo Gebang - Pulo Gadung 2 via PIK0.0B05587PTaman Elok 1-6.195743106.93526232023-04-03 05:44:51B03090PRaya Penggilingan-6.183068106.9319429.02023-04-03 06:28:163500.0
4VMLO535V7F95NJ570928206772flazzEmbuh MardhiyahM198212Tanjung Priok - Pluit0.0P00239Sunter Boulevard Barat-6.149650106.8890052023-04-03 06:17:35P00098Kali Besar Barat-6.135355106.8114315.02023-04-03 06:57:033500.0
5DDES630K2F80KC2251412124634980dkiTirta SiregarF19931TCibubur - Balai Kota0.0B00127PBenhil 3-6.216010106.8163232023-04-03 05:08:01B00694PGrand Sahid-6.210975106.820596.02023-04-03 05:52:2520000.0
6HEMW326B9N91TV213155822653833emoneyR. Devi Hariyah, S.T.M1974JAK.18Kalibata - Kuningan0.0B00243PCervino Village-6.224355106.85165252023-04-03 06:58:21NaNNaNNaNNaNNaNNaN0.0
7XTKE052E5E87LN3587341902618993dkiDarmanto RajasaF1991NaNNaN1.0B03416PSDN Pondok Labu 11-6.313269106.8022122023-04-03 06:44:24B00899PJln. Baros-6.311046106.7840012.02023-04-03 07:11:430.0
8OIHS248V7S72EB6510013988638519dkiCagak Maheswara, S.SosF1992B14Bekasi Barat - Kuningan1.0B00795PImperium-6.210363106.8305912023-04-03 06:01:44B01853PKayuringin 2-6.245948106.9924716.02023-04-03 06:51:1220000.0
9ERXO372B2H63RB3580401035990896dkiT. Dadap Pradana, M.AkM20041QRempoa - Blok M0.0B05781PTPU Bungur-6.247208106.77889152023-04-03 05:57:47B06578PBlok M Jalur 3-6.243426106.8018824.02023-04-03 06:51:123500.0
transIDpayCardIDpayCardBankpayCardNamepayCardSexpayCardBirthDatecorridorIDcorridorNamedirectiontapInStopstapInStopsNametapInStopsLattapInStopsLonstopStartSeqtapInTimetapOutStopstapOutStopsNametapOutStopsLattapOutStopsLonstopEndSeqtapOutTimepayAmount
37890GFLN726N6E23SR213142775112736emoneyNadine WaluyoF1997JAK.30Grogol - Meruya via Roxy0.0B00018PAKR Tower-6.190732106.76743322023-04-30 14:45:49B02471PMNC Studios-6.190376106.7662533.02023-04-30 15:09:390.0
37891XQDZ821C9G88JD2712823212983860dkidrg. Maras Wibowo, S.KedF1966JAK.30Grogol - Meruya via Roxy1.0B01209PJln. Kartika Meruya Selatan-6.203099106.73673142023-04-19 16:31:09B04071PSbr. Masjid Nurul Falah Grogol-6.170757106.7847753.02023-04-19 19:13:530.0
37892YHJT665I6G08OS4775206940093onlineCahyo Sudiati, M.FarmM1979JAK.52Terminal Kalideres - Terminal Muara Angke1.0B01087PJln. Gunung Galunggung 1-6.142286106.73712292023-04-28 16:38:56B03207PRSUD Cengkareng-6.142238106.7339830.02023-04-28 18:35:160.0
37893GNET512K3A93CA3500965207195341dkiLatika SalahudinF2000JAK.80Rawa Buaya - Rawa Kompeni0.0B02485PMTsN 37-6.097910106.70237392023-04-19 08:16:10B03053PPuskesmas Kel. Kamal II-6.100938106.6978642.02023-04-19 10:32:300.0
37894ZXVG342K6T27GU4475487986105118550brizziTgk. Dipa Purnawati, S.E.IF2012JAK.39Kalimalang - Duren Sawit0.0B04489PSbr. SMPN 252-6.239289106.94229272023-04-14 11:36:12B01624PJln. Swakarsa III Pondok Kelapa-6.244759106.9424935.02023-04-14 13:15:250.0
37895ZWEC949B8Q87QG4685818286724028395brizziKamila MahendraF20046BRagunan - MH Thamrin via Semanggi1.0P00261Tosari-6.196892106.8230922023-04-21 18:18:37P00228SMK 57-6.290967106.8236513.02023-04-21 19:55:493500.0
37896YHHK837P6Y95GN6502902290603767dkiTiti SiregarM19749NPinang Ranti - Pramuka1.0P00064Garuda Taman Mini-6.290154106.8811612023-04-18 21:52:31P00179Pinang Ranti-6.291075106.886342.02023-04-18 22:28:223500.0
37897YXPP627N4G95HO213159426675861emoneydrg. Zahra NashiruddinF19761TCibubur - Balai Kota1.0B02873PPlaza Sentral-6.216247106.81676122023-04-04 10:29:47B00226PBuperta Cibubur-6.370321106.8962814.02023-04-04 13:27:2520000.0
37898RGVK175U2U98UV377840859133591emoneyAna AgustinaM1976JAK.13Tanah Abang - Jembatan Lima1.0B02505PMuseum Textile-6.188656106.80954332023-04-15 19:59:26B01787PJPO Blok G-6.188861106.8113534.02023-04-15 20:27:500.0
37899FMZZ963S4B68ZP501862539795flazzdrg. Leo NajmudinF198513Ciledug - Tendean0.0P00001Adam Malik-6.236466106.7478622023-04-12 21:08:12P00106Kebayoran Lama-6.238340106.777527.02023-04-12 21:34:533500.0